Dach1 scatter plot
markers_for_heatmap <- function(markers) {
res <- NULL
for (i in unique(markers[, 1])) {
tmp <- markers[markers[, 1] == i, ]
if (nrow(tmp) > 10) {
res <- rbind(res, tmp[1:10, ])
} else {
res <- rbind(res, tmp)
}
}
return(res)
}
organise_marker_genes <- function(object, k, p_val, auroc) {
dat <- rowData(object)[, c(paste0("sc3_", k, "_markers_clusts"), paste0("sc3_", k,
"_markers_auroc"), paste0("sc3_", k, "_markers_padj"), "feature_symbol")]
dat <- dat[dat[, paste0("sc3_", k, "_markers_padj")] < p_val & !is.na(dat[, paste0("sc3_",
k, "_markers_padj")]), ]
dat <- dat[dat[, paste0("sc3_", k, "_markers_auroc")] > auroc, ]
d <- NULL
for (i in sort(unique(dat[, paste0("sc3_", k, "_markers_clusts")]))) {
tmp <- dat[dat[, paste0("sc3_", k, "_markers_clusts")] == i, ]
tmp <- tmp[order(tmp[, paste0("sc3_", k, "_markers_auroc")], decreasing = TRUE), ]
d <- rbind(d, tmp)
}
return(d)
}
make_col_ann_for_heatmaps <- function(object, show_pdata) {
if (any(!show_pdata %in% colnames(colData(object)))) {
show_pdata_excl <- show_pdata[!show_pdata %in% colnames(colData(object))]
show_pdata <- show_pdata[show_pdata %in% colnames(colData(object))]
message(paste0("Provided columns '", paste(show_pdata_excl, collapse = "', '"), "' do not exist in the phenoData table!"))
if (length(show_pdata) == 0) {
return(NULL)
}
}
ann <- NULL
if (is.null(metadata(object)$sc3$svm_train_inds)) {
ann <- colData(object)[, colnames(colData(object)) %in% show_pdata]
} else {
ann <- colData(object)[metadata(object)$sc3$svm_train_inds, colnames(colData(object)) %in%
show_pdata]
}
# remove columns with 1 value only
if (length(show_pdata) > 1) {
keep <- unlist(lapply(ann, function(x) {
length(unique(x))
})) > 1
if (!all(keep)) {
message(paste0("Columns '", paste(names(keep)[!keep], collapse = "', '"), "' were excluded from annotation since they contained only a single value."))
}
ann <- ann[, names(keep)[keep]]
if (ncol(ann) == 0) {
ann <- NULL
} else {
ann <- as.data.frame(lapply(ann, function(x) {
if (nlevels(as.factor(x)) > 9)
x else as.factor(x)
}))
# convert outlier scores back to numeric
for (i in grep("_log2_outlier_score", colnames(ann))) {
if (class(ann[, i]) == "factor") {
ann[, i] <- as.numeric(levels(ann[, i]))[ann[, i]]
}
}
}
} else {
if (length(unique(ann)) > 1) {
ann <- as.data.frame(ann)
colnames(ann) <- show_pdata
if (!grepl("_log2_outlier_score", show_pdata)) {
ann <- as.data.frame(lapply(ann, function(x) {
if (nlevels(as.factor(x)) > 9)
return(x) else return(as.factor(x))
}))
}
} else {
message(paste0("Column '", show_pdata, "' was excluded from annotation since they contained only a single value."))
ann <- NULL
}
}
return(ann)
}
sc3_plot_markers.new <- function(object, k, auroc, p.val, show_pdata) {
if (is.null(metadata(object)$sc3$consensus)) {
warning(paste0("Please run sc3_consensus() first!"))
return(object)
}
hc <- metadata(object)$sc3$consensus[[as.character(k)]]$hc
dataset <- get_processed_dataset(object)
if (!is.null(metadata(object)$sc3$svm_train_inds)) {
dataset <- dataset[, metadata(object)$sc3$svm_train_inds]
}
add_ann_col <- FALSE
ann <- NULL
if (!is.null(show_pdata)) {
ann <- make_col_ann_for_heatmaps(object, show_pdata)
if (!is.null(ann)) {
add_ann_col <- TRUE
# make same names for the annotation table
rownames(ann) <- colnames(dataset)
}
}
ann_colors = rep(list(c("grey","white", "firebrick2")),ncol(ann)-sum(sapply(ann, is.factor)))
names(ann_colors) = colnames(ann)[!(sapply(ann, is.factor))]
# get all marker genes
markers <- organise_marker_genes(object, k, p.val, auroc)
# get top 10 marker genes of each cluster
markers <- markers_for_heatmap(markers)
row.ann <- data.frame(Cluster = factor(markers[, 1], levels = unique(markers[, 1])))
rownames(row.ann) <- markers$feature_symbol
do.call(pheatmap::pheatmap, c(list(dataset[markers$feature_symbol, , drop = FALSE], show_colnames = FALSE,
cluster_rows = FALSE, cluster_cols = hc, cutree_cols = k, annotation_row = row.ann, annotation_colors = ann_colors, annotation_names_row = FALSE, cellheight = 10), list(annotation_col = ann)[add_ann_col]))
}load the data
source("sc_func.R")
suppressMessages(library(Rtsne))
suppressMessages(library(destiny))
suppressMessages(library(ggplot2))package ‘ggplot2’ was built under R version 3.4.4suppressMessages(library(gplots))
suppressMessages(library(RColorBrewer))
suppressMessages(library(plotly))
suppressMessages(library(gridExtra))
hmcol<-colorRampPalette(c("blue", "white","red"))(n = 299)
load("sc_192.RData")heatmap
do PCA+diffusionmap and extract genes correlated with top PC, excepct PCs that related to cell cycles:
pca_out = prcomp(t(log2(expr_data+1)))
dif <- DiffusionMap( pca_out$x[,c(2,3,5,7,8,9,10,11,12,13,14)],n_eigs = 10)
dif_GO_list = Diff_GO_enrichment(dif,expr_data,p.val=0.001)
Building most specific GOs .....
Loading required package: org.Mm.eg.db
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 2492 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 2097 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1759 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1577 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1093 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1156 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1344 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 1594 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 872 nontrivial nodes
parameters:
test statistic: fisher
Building most specific GOs .....
( 9332 GO terms found. )
Build GO DAG topology ..........
( 13302 GO terms and 31005 relations. )
Annotating nodes ...............
( 9474 genes annotated to the GO terms. )
-- Classic Algorithm --
the algorithm is scoring 762 nontrivial nodes
parameters:
test statistic: fisherdiff_genes = c()
for(i in 1:10){diff_genes = c(diff_genes,rownames(dif_GO_list$DE_genes[[i]]))}
DE_expr = expr_data[rownames(expr_data) %in% diff_genes,]
DE_clean = DE_expr[apply(log2(DE_expr+1),1,mean)<6,]
DE_clean = DE_clean[rowMeans(DE_clean)>0.6,]
dim(DE_expr)[1] 407 187hr <- hclust(dist(cor(log2(DE_clean+1))))
tsne_ph = Rtsne(log10(ph_data[,!(colnames(ph_data) %in% c("CD11b"))]),pca = FALSE,perplexity = 10)
tsne_ph1 = Rtsne(log10(ph_data[,!(colnames(ph_data) %in% c("CD11b"))]),dim=1,pca = FALSE,perplexity = 10)
# define some clusters
mycl <- cutree(hr, k=10)
table(mycl)mycl
1 2 3 4 5 6 7 8 9 10
10 4 42 31 46 35 6 2 10 1 #mycl[mycl>8] = 8 # exclude rare cluster
# get a color palette equal to the number of clusters
#clusterCols <- rainbow(length(unique(mycl)))
clusterCols = brewer.pal(10, "Set3")
# create vector of colors for side bar
myClusterSideBar <- clusterCols[mycl]
#ph_data$cluster = myClusterSideBar
DE_hm = heatmap.2(log2(DE_clean[,order(mycl)]+1),
Colv=FALSE,
trace="none",
dendrogram = "none",
col=hmcol,
density.info = "none",
labCol = "",
labRow = "",
ColSideColors = myClusterSideBar[order(mycl)],
key = FALSE
)
ma = log2(DE_clean[,order(mycl)]+1)
ma = ma[DE_hm$rowInd,]
plot_ly(
x = colnames(ma), y = rownames(ma),
z = ma, type = "heatmap",colorscale = hmcol
)database = "immgen"
immgen_samples <- read.delim(paste0("/Users/tian.l/Dropbox/research/Dach1_paper/ref_data/",database,"_samples.txt"), stringsAsFactors=FALSE)
immgen_probes <- read.delim(paste0("/Users/tian.l/Dropbox/research/Dach1_paper/ref_data/",database,"_probes.txt"), header=FALSE, stringsAsFactors=FALSE)
immgen_expression <- read.delim(paste0("/Users/tian.l/Dropbox/research/Dach1_paper/ref_data/",database,"_expression.txt"), stringsAsFactors=FALSE)
library('biomaRt')
mart <- useDataset("mmusculus_gene_ensembl", useMart("ensembl"))
G_list <- getBM(filters= "ensembl_gene_id", attributes= c("ensembl_gene_id", "external_gene_name", "description"),values=immgen_probes$V2 ,mart=mart)
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immgen_probes = immgen_probes[immgen_probes$V2 %in% G_list$ensembl_gene_id,]
G_list = G_list[match(immgen_probes$V2, G_list$ensembl_gene_id),]
immgen_probes$external_gene_name = G_list$external_gene_name
immgen_expression = immgen_expression[immgen_expression$probeId %in% immgen_probes$V1,]
immgen_probes = immgen_probes[immgen_probes$V1 %in% immgen_expression$probeId,]
immgen_probes = immgen_probes[match(immgen_expression$probeId, immgen_probes$V1),]
immgen_expression$gene_id = immgen_probes$external_gene_name
immgen_expression = immgen_expression[!duplicated(immgen_expression$gene_id),]
g_i = immgen_expression$gene_id
immgen_expression = immgen_expression[,!(colnames(immgen_expression) %in% c("probeId","gene_id"))]
immgen_expression = as.matrix(immgen_expression)
rownames(immgen_expression) = g_i
table(immgen_samples$cell_lineage)
ab T cells B cells Dendritic cells gd T cells Innate Lymphocytes Macrophages
67 26 36 22 22 15
Monocytes Neutrophils Stem Cells Stromal cells
8 6 14 8 immgen_samples$sampleId = gsub("-",".",immgen_samples$sampleId)
#SC_list = immgen_samples[immgen_samples$cell_lineage == "Stem Cells", "sampleId"]
#SC_list = immgen_samples$sampleId
SC_list = c("SC_CDP_BM","SC_CMP_BM","SC_GMP_BM",
"SC_LT34F_BM","SC_LTSL_BM","SC_MDP_BM",
"SC_MEP_BM","SC_MPP34F_BM","SC_ST34F_BM",
"SC_STSL_BM","proB_CLP_BM","preT_ETP_Th")
sub_immgen_expression = immgen_expression[rownames(immgen_expression) %in% rownames(DE_expr), colnames(immgen_expression) %in% SC_list]
sub_immgen_expression = sub_immgen_expression[!duplicated(rownames(sub_immgen_expression)),]
sub_DE_expr = DE_expr[rownames(DE_expr) %in% rownames(sub_immgen_expression),]
sub_DE_expr = sub_DE_expr[!duplicated(rownames(sub_DE_expr)),]
sub_DE_expr = sub_DE_expr[order(rownames(sub_DE_expr)),]
sub_immgen_expression = sub_immgen_expression[order(rownames(sub_immgen_expression)),]
p.val_mat = c()
for (i in 1:ncol(sub_immgen_expression))
{
p.val_vec = apply(sub_DE_expr, 2, function(x){
cor.test(x,sub_immgen_expression[,i],
method = "spearman",
alternative = "greater",exact=FALSE)$p.value})
p.val_mat = rbind(p.val_mat,-log10(p.val_vec))
}
rownames(p.val_mat) = colnames(sub_immgen_expression)
hm_immgen = heatmap.2(p.val_mat[,order(mycl)],trace="none",
dendrogram="none",
Colv=FALSE,
col=hmcol,
scale="column",
labRow = "",
#ColSideColors = myClusterSideBar[order(mycl)],
labCol = "",
density.info="none",
key=FALSE)
p_ma = p.val_mat[,order(mycl)]
p_ma = p_ma[hm_immgen$rowInd,]
m = list(
l = 100,
r = 40,
b = 10,
t = 10,
pad = 0
)
plot_ly(
x = colnames(p_ma), y = rownames(p_ma),
z = p_ma, type = "heatmap",colorscale = hmcol
)%>%
layout(autosize = F, margin = m)combine two heatmap:
library(ComplexHeatmap)Loading required package: grid
Attaching package: ‘grid’
The following object is masked from ‘package:topGO’:
depth
========================================
ComplexHeatmap version 1.17.1
Bioconductor page: http://bioconductor.org/packages/ComplexHeatmap/
Github page: https://github.com/jokergoo/ComplexHeatmap
Documentation: http://bioconductor.org/packages/ComplexHeatmap/
If you use it in published research, please cite:
Gu, Z. Complex heatmaps reveal patterns and correlations in multidimensional
genomic data. Bioinformatics 2016.
========================================
Attaching package: ‘ComplexHeatmap’
The following object is masked from ‘package:plotly’:
add_heatmapht1 = Heatmap(t(p.val_mat), name = "Immgen", show_row_names=FALSE, width = 1)
ht2 = Heatmap(t(log2(DE_clean+1)), name = "RNA-seq", show_row_names=FALSE, show_column_names=FALSE, width = 2)
ht3 = Heatmap(scale(log10(ph_data[,!(colnames(ph_data)=="CD11b")]+1)), name = "FACS",show_row_names=FALSE, width = 0.5)
ht_list = draw(ht3+ht2+ht1, main_heatmap = "RNA-seq", km = 10,show_row_dend=FALSE)
#clusterCols = brewer.pal(10, "Set3")
# create vector of colors for side bar
#myClusterSideBar <- clusterCols[mycl]
ht_list
t-SNE
tsne_diffmap_DE = Rtsne(cor(log2(DE_expr+1)))
tsne_diffmap_DE1 = Rtsne(cor(log2(DE_expr+1)),dim=1)
plot(tsne_diffmap_DE$Y[,1], tsne_diffmap_DE$Y[,2])
plot(tsne_diffmap_DE1$Y, tsne_ph1$Y)
Dach1 correlation
cor_shalin =cor(t(expr_data),method="spearman")
cor_Dach1 = cor_shalin["Dach1",]
cor_Dach1 = cor_Dach1[order(cor_Dach1)]genes that positively correlated with Dach1:
as.data.frame(head(cor_Dach1[order(cor_Dach1,decreasing=TRUE)],n=50))some scatter plot:
g_e = as.data.frame(t(log2(expr_data+1)))
p1=ggplot(data=g_e,aes(y=Dach1, x=Muc13))+geom_point()
p2=ggplot(data=g_e,aes(y=Dach1, x=Gata2))+geom_point()
p3=ggplot(data=g_e,aes(y=Dach1, x=Cdk17))+geom_point()
p4=ggplot(data=g_e,aes(y=Dach1, x=Sox6))+geom_point()
grid.arrange(p1, p2, p3, p4, ncol=2)Error: `data` must be uniquely named but has duplicate elements
genes that negatively correlated with Dach1:
some scatter plot:
p1=ggplot(data=g_e,aes(y=Dach1, x=Evl))+geom_point()
p2=ggplot(data=g_e,aes(y=Dach1, x=Il12a))+geom_point()
p3=ggplot(data=g_e,aes(y=Dach1, x=Dntt))+geom_point()
p4=ggplot(data=g_e,aes(y=Dach1, x=Flt3))+geom_point()
grid.arrange(p1, p2, p3, p4, ncol=2)Error: `data` must be uniquely named but has duplicate elements
Dach1 correlation in public data
data from Gottgens’s Blood paper, we only select cells labeled as “HSC” and “LT-HSC”.
coordinates_gene_counts_flow_cytometry <- read.delim("/Users/tian.l/Dropbox/research/Dach1_paper/public_data/coordinates_gene_counts_flow_cytometry.txt", row.names=1, stringsAsFactors=FALSE)
coordinates_gene_counts_flow_cytometry = coordinates_gene_counts_flow_cytometry[complete.cases(coordinates_gene_counts_flow_cytometry),]
gene_expr = coordinates_gene_counts_flow_cytometry[,-(1:26)]
phen_data = coordinates_gene_counts_flow_cytometry[,6:15]
gene_expr_t = t(gene_expr)
G_cor = cor(t(gene_expr_t))
library('biomaRt')
mart <- useDataset("mmusculus_gene_ensembl", useMart("ensembl"))
G_list <- getBM(filters= "ensembl_gene_id", attributes= c("ensembl_gene_id", "external_gene_name", "description"),values=rownames(gene_expr_t) ,mart= mart)
gene_expr_t = gene_expr_t[rownames(gene_expr_t) %in% G_list$ensembl_gene_id,]
G_list = G_list[match(rownames(gene_expr_t), G_list$ensembl_gene_id),]
rownames(gene_expr_t) = G_list$external_gene_name
gene_expr_sub = gene_expr_t[,coordinates_gene_counts_flow_cytometry$group %in% c("HSC", "LT-HSC")]
corr_sub = cor(t(gene_expr_sub), method="spearman")
cor_Dach1_pub = corr_sub["Dach1",]
cor_Dach1_pub = cor_Dach1_pub[order(cor_Dach1_pub)]genes that positively correlated with Dach1:
as.data.frame(head(cor_Dach1_pub[order(cor_Dach1_pub,decreasing=TRUE)],n=50))genes that negatively correlated with Dach1:
as.data.frame(head(cor_Dach1_pub[order(cor_Dach1_pub)],n=50))Genes that negatively correlated to Dach1 in both dataset (first 50):
neg = toupper(names(head(cor_Dach1_pub[order(cor_Dach1_pub)],n=50)))
neg = neg[neg %in% toupper(names(head(cor_Dach1[order(cor_Dach1)],n=50)))]
negGenes that positively correlated to Dach1 in both dataset (first 50):
pos = toupper(names(head(cor_Dach1_pub[order(cor_Dach1_pub,decreasing=TRUE)],n=50)))
pos = pos[pos %in% toupper(names(head(cor_Dach1[order(cor_Dach1,decreasing=TRUE)],n=50)))]
poscell cycle
Cells are classified as being in G1 phase if the G1 score is above 0.5 and greater than the G2/M score; in G2/M phase if the G2/M score is above 0.5 and greater than the G1 score; and in S phase if neither score is above 0.5.
library(scran)
mm.pairs <- readRDS(system.file("exdata", "mouse_cycle_markers.rds", package="scran"))
library(org.Mm.eg.db)
anno <- select(org.Mm.eg.db, keys=rownames(expr_data), keytype="SYMBOL", column="ENSEMBL")
ensembl <- anno$ENSEMBL[match(rownames(expr_data), anno$SYMBOL)]
assignments <- cyclone(log2(expr_data+1), mm.pairs, gene.names=ensembl)
ggplot(data=NULL, aes(x=assignments$score$G1, y=assignments$score$G2M, col=log2(expr_data["Dach1",]+1)))+
geom_point()+
geom_vline(xintercept = .5)+
geom_hline(yintercept = .5)
cell_state = rep("S",ncol(expr_data))
cell_state[assignments$score$G1>0.5] = "G0/G1"
cell_state[assignments$score$G2M>0.5] = "G2/M"
dach1 = log2(expr_data["Dach1",]+1)
ggplot(data=NULL,aes(x=cell_state,y=dach1,fill=cell_state))+
geom_violin(alpha=0.7) + geom_jitter(height = 0, width = 0.1)+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())public_assignments <- cyclone(t(gene_expr)[,coordinates_gene_counts_flow_cytometry$group %in% c("HSC", "LT-HSC")], mm.pairs, gene.names=rownames(t(gene_expr)))
ggplot(data=NULL, aes(x=public_assignments$score$G1, y=public_assignments$score$G2M, col=gene_expr_t["Dach1",coordinates_gene_counts_flow_cytometry$group %in% c("HSC", "LT-HSC")]))+
geom_point()+
labs(x="G1",y="G2M",color="Dach1 expr")+
geom_vline(xintercept = .5)+
geom_hline(yintercept = .5)cell_state = rep("S",sum(coordinates_gene_counts_flow_cytometry$group %in% c("HSC", "LT-HSC")))
cell_state[public_assignments$score$G1>0.5] = "G0/G1"
cell_state[public_assignments$score$G2M>0.5] = "G2/M"
dach1 = gene_expr_t["Dach1",]
ggplot(data=NULL,aes(x=cell_state,y=dach1,fill=cell_state))+
geom_violin(alpha=0.7) + geom_jitter(height = 0, width = 0.1)+
theme_bw()+
theme(panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.border = element_blank())sceset <- sc3(sceset, ks = 4:8, biology = TRUE,pct_dropout_max=95,rand_seed=19910603,kmeans_nstart=50)
Setting SC3 parameters...
Calculating distances between the cells...starting worker pid=20265 on localhost:11927 at 11:29:35.450
starting worker pid=20273 on localhost:11927 at 11:29:35.660
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Performing transformations and calculating eigenvectors...starting worker pid=20293 on localhost:11927 at 11:29:41.435
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Performing k-means clustering...starting worker pid=20348 on localhost:11927 at 11:29:50.050
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|==========================================================================================================================| 100%Calculating consensus matrix...starting worker pid=20413 on localhost:11927 at 11:30:01.318
starting worker pid=20421 on localhost:11927 at 11:30:01.554
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starting worker pid=20445 on localhost:11927 at 11:30:02.248Loading required package: SC3
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Calculating biology...starting worker pid=20459 on localhost:11927 at 11:30:08.867
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No outliers detected in cluster 1. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 1. Distribution of gene expression in cells is too skewed towards 0.
The covariance matrix has become singular during
the iterations of the MCD algorithm.
There are 263 observations (in the entire dataset of 323 obs.) lying on
the hyperplane with equation a_1*(x_i1 - m_1) + ... + a_p*(x_ip - m_p)
= 0 with (m_1, ..., m_p) the mean of these observations and
coefficients a_i from the vector a <- c(0.3565493, 0.5424786,
0.3488608, -0.5984084, -0.3079517, 0.0590775, -0.0216488)The covariance matrix has become singular during
the iterations of the MCD algorithm.
There are 0 observations (in the entire dataset of 323 obs.) lying on
the hyperplane with equation a_1*(x_i1 - m_1) + ... + a_p*(x_ip - m_p)
= 0 with (m_1, ..., m_p) the mean of these observations and
coefficients a_i from the vector a <- c(-0.3254948, 0.5212834,
-0.6797406, 0.0837222, -0.3883333, 0.0299857, 0.0394742)No outliers detected in cluster 3. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 2. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 2. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 2. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 3. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 3. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 4. Distribution of gene expression in cells is too skewed towards 0.
The covariance matrix has become singular during
the iterations of the MCD algorithm.
There are 0 observations (in the entire dataset of 323 obs.) lying on
the hyperplane with equation a_1*(x_i1 - m_1) + ... + a_p*(x_ip - m_p)
= 0 with (m_1, ..., m_p) the mean of these observations and
coefficients a_i from the vector a <- c(0.3254948, -0.5212834,
0.6797406, -0.0837222, 0.3883333, -0.0299857, -0.0394742)No outliers detected in cluster 6. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 7. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 5. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 5. Distribution of gene expression in cells is too skewed towards 0.
No outliers detected in cluster 8. Distribution of gene expression in cells is too skewed towards 0.sceset <- sc3_estimate_k(sceset)Estimating k...sceset$SC_LTSL_BM = p.val_mat["SC_LTSL_BM",]
sceset$SC_STSL_BM = p.val_mat["SC_STSL_BM",]
sceset$SC_MEP_BM = p.val_mat["SC_MEP_BM",]
sceset$SC_CMP_BM = p.val_mat["SC_CMP_BM",]
sceset$proB_CLP_BM = p.val_mat["proB_CLP_BM",]
sceset$SC_GMP_BM = p.val_mat["SC_GMP_BM",]
sceset$CD150 = log2(ph_data[, "CD150"]+1)
sceset$CD135 = log2(ph_data[, "CD135"]+1)
sceset$CD135[(sceset)$CD135<5] = 5
sceset$CD127 = log2(ph_data[, "CD127"]+1)
sceset$CD127[(sceset)$CD127<5] = 5
sceset$CD16.32 = log2(ph_data[, "CD16.32"]+1)
sceset$Sca1 = log2(ph_data[, "Sca.1"]+1)
sceset$cKit = log2(ph_data[, "cKit"]+1)
#sceset$G1 = assignments$scores$G1
#sceset$G2M = assignments$scores$G2Mpdf("Dach1_clusters_IMMGEN.pdf",width = 8,height = 10)
sc3_plot_markers(sceset, k = 7,show_pdata = c(
"SC_LTSL_BM",
"SC_STSL_BM",
"SC_MEP_BM",
"SC_CMP_BM",
"proB_CLP_BM",
"SC_GMP_BM",
"sc3_7_clusters"
), p.val=0.01,auroc=0.75)
dev.off()pdf("Dach1_clusters_FACs.pdf",width = 8,height = 9)
sc3_plot_markers(sceset, k = 7,show_pdata = c(
"CD150",
"CD135",
"CD127",
"CD16.32",
"G1",
"G2M"
), p.val=0.01,auroc=0.75)
dev.off()pdf("Dach1_clusters_all.pdf",width = 8,height = 16)
sc3_plot_markers.new(sceset, k = 5,show_pdata = c(
"CD150",
"CD135",
"CD127",
"CD16.32",
"SC_LTSL_BM",
"SC_STSL_BM",
"SC_MEP_BM",
"SC_CMP_BM",
"proB_CLP_BM",
"SC_GMP_BM"
), p.val=0.05,auroc=0.6)
dev.off()
RStudioGD
2 library(edgeR)
y = DGEList(counts=expr_data)
y = estimateCommonDisp(y)
design <- model.matrix(~log(expr_data["Dach1",]+1))
fit = glmFit(y,design=design)
lrt = glmLRT(fit,coef = 2)
t = topTags(lrt,n=Inf)
test_df = t@.Data[[1]]
test_df = test_df[!(rownames(test_df)=="Dach1"),]
test_df$FDR = -log10(test_df$FDR)
plot(test_df$logFC, test_df$FDR)
test_df["Gata2",]library(scran)
sceall <- newSCESet(countData=expr_data[!duplicated(rownames(expr_data)),])
sceall = calculateQCMetrics(sceall)
var.fit <- trendVar(sceall, trend="loess", use.spikes=FALSE, span=0.2)
var.out <- decomposeVar(sceall, var.fit)
hvg.out <- var.out[order(var.out$bio, decreasing=TRUE)[1:2000],]scran_cor = correlatePairs(sceall, subset.row=rownames(hvg.out),BPPARAM=MulticoreParam(workers = 4))
save(scran_cor,file="/Users/tian.l/Dropbox/research/Dach1_paper/scran_cor_top2000.RData")scran_cor_dach1 = scran_cor[scran_cor$gene1 == "Dach1" | scran_cor$gene2 == "Dach1",]
the_cor_genes = rep(NA, nrow(scran_cor_dach1))
the_cor_genes[scran_cor_dach1$gene1 == "Dach1"] = scran_cor_dach1$gene2[scran_cor_dach1$gene1 == "Dach1"]
the_cor_genes[scran_cor_dach1$gene2 == "Dach1"] = scran_cor_dach1$gene1[scran_cor_dach1$gene2 == "Dach1"]
library(readr)
renameME <- read_csv("~/Dropbox/research/Dach1_paper/renameME_LTMPP.csv")
renameME_DE = renameME[renameME$adj.P.Val<0.05,]
renameME_DE_CMP = renameME_DE[renameME_DE$FC>1,]
renameME_DE_CLP = renameME_DE[renameME_DE$FC<1,]
tmp = scran_cor_dach1$rho[scran_cor_dach1$FDR<0.05]
pdf("barcode_plot_Dach1_immgen_LT_MPP.pdf")
barcodeplot(scran_cor_dach1$rho, index=(the_cor_genes %in% renameME_DE_CMP$Gene_Symbol ),
index2=(the_cor_genes %in% renameME_DE_CLP$Gene_Symbol ), main="LTvsMPP34",
labels=c("negative correlation","positive correlation"),
quantiles=c(max(tmp[tmp<0]),min(tmp[tmp>0])),
xlab="speaman correlation to Dach1 expressions"
)
dev.off()hm20 = colorRampPalette(c("blue", "white","red"))(n = 20)
side_bar = c("orchid1", "steelblue1")[as.factor(scran_cor_dach1$rho[scran_cor_dach1$FDR<0.05]>0)]
names(side_bar) = "rho to Dach1"
top_dach1_DE = the_cor_genes[scran_cor_dach1$FDR<0.05]
pdf("top_correlated_to_Dach1.pdf")
heatmap.2(cor(t(log2(expr_data[top_dach1_DE,]+1)),method = "spearman"),
trace = "none",
col=hmcol,
main="Correlation of genes correlated with Dach1",
ColSideColors=side_bar,
RowSideColors=side_bar,
key.title="speaman rho")
legend("topright",
legend = c("positive correlated to Dach1", "negatively correlated to Dach1"),
col = c("steelblue1","orchid1"),
lty= 1.5, lwd = 2,
cex=.6)
dev.off()CODEX[CODEX$Factor %in% the_cor_genes,]
scran_cor_dach1[the_cor_genes %in% CODEX$Factor,]TF_genes = scPipe::get_genes_by_GO(returns="external_gene_name",
dataset="mmusculus_gene_ensembl",
go="GO:0003700")pdf("FACS_overlay.pdf",width = 4,height = 3.5)
# cluster = as.factor(sceset$sc3_5_clusters == 1)
# ggplot(data=as.data.frame(colData(sceset)),aes(x=CD135,y=CD150,col=cluster))+
# theme_bw()+
# geom_point()+
# scale_color_manual(values=c("grey80","red"))+
# #labs(color="stem")+
# ggtitle("stem")
#
# cluster = as.factor(sceset$sc3_5_clusters %in% c(1,2))
# ggplot(data=as.data.frame(colData(sceset)),aes(x=CD135,y=CD150,col=cluster))+
# theme_bw()+
# geom_point()+
# scale_color_manual(values=c("grey80","red"))+
# #labs(color="stem+MEP")+
# ggtitle("stem+MEP")
#
# cluster = as.factor(sceset$sc3_5_clusters %in% c(1,2,3))
# ggplot(data=as.data.frame(colData(sceset)),aes(x=CD135,y=CD150,col=cluster))+
# theme_bw()+
# geom_point()+
# scale_color_manual(values=c("grey80","red"))+
# #labs(color="stem+MEP+myeloid")+
# ggtitle("stem+MEP+myeloid")
ggplot(data=as.data.frame(colData(sceset))[sceset$cluster %in% c(4),],aes(x=CD150,y=CD135,col=cluster))+
theme_minimal()+
geom_point()+
xlim(3.5, 10)+
ylim(5, 13)+
scale_color_manual(values=darkcols[c(4)])+
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
ggtitle("stem")
ggplot(data=as.data.frame(colData(sceset))[sceset$cluster %in% c(2,4),],aes(x=CD150,y=CD135,col=cluster))+
theme_minimal()+
geom_point()+
xlim(3.5, 10)+
ylim(5, 13)+
scale_color_manual(values=darkcols[c(2,4)])+
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
ggtitle("stem+MEP")
ggplot(data=as.data.frame(colData(sceset))[sceset$cluster %in% c(2,3,4),],aes(x=CD150,y=CD135,col=cluster))+
theme_minimal()+
geom_point()+
xlim(3.5, 10)+
ylim(5, 13)+
scale_color_manual(values=darkcols[c(2,3,4)])+
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
ggtitle("stem+MEP+myeloid")
ggplot(data=as.data.frame(colData(sceset)[sceset$cluster %in% c(1,2,3,4),]),aes(x=CD150,y=CD135,col=cluster))+
theme_minimal()+
geom_point()+
xlim(3.5, 10)+
ylim(5, 13)+
scale_color_manual(values=darkcols)+
theme(panel.border = element_blank(), panel.grid.major = element_blank(),
panel.grid.minor = element_blank(), axis.line = element_line(colour = "black"))+
ggtitle("stem+MEP+myeloid+lymphoid")
dev.off()
RStudioGD
2 pdf("FACS_plot_per_clusters.pdf")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 1)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 1")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 1")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 1")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 2)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 2")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 2")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 2")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 3)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 3")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 3")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 3")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 4)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 4")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 4")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 4")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 5)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 5")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 5")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 5")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 6)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 6")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 6")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 6")
cluster = as.factor(pData(sceset)$sc3_7_clusters == 7)
ggplot(data=pData(sceset),aes(x=CD135,y=CD150,col=cluster))+geom_point()+ggtitle("cluster 7")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD127,col=cluster))+geom_point()+ggtitle("cluster 7")
ggplot(data=pData(sceset),aes(x=Sca1,y=CD16.32,col=cluster))+geom_point()+ggtitle("cluster 7")
dev.off()
y = DGEList(counts=expr_data)
y = estimateCommonDisp(y)
design <- model.matrix(~cell_state)
fit = glmFit(y,design=design)
lrt = glmLRT(fit,coef = 2)
cc_genes = rownames(topTags(lrt,n=Inf,p.value=0.05))
lrt = glmLRT(fit,coef = 3)
cc_genes = c(cc_genes, rownames(topTags(lrt,n=Inf,p.value=0.05)))sce_hi = sceall[rownames(hvg.out),]
#sce_hi = sce_hi[!(rownames(sce_hi) %in% cc_genes),]sce_hi <- sc3(sce_hi, ks = 4:10, biology = TRUE)
sce_hi <- sc3_estimate_k(sce_hi)
pData(sce_hi)$G1 = assignments$scores$G1
pData(sce_hi)$G2M = assignments$scores$G2Msc3_plot_markers(sce_hi, k = 7,show_pdata = c(
"G1",
"G2M"
), p.val=0.01,auroc=0.75)revisit t-SNE plots
ggplotly(ggplot(data=NULL,aes(x=tsne_sc3dim1$Y[,1],y=rtsne_ph$Y[,1],col=as.factor(pData(sceset)$sc3_8_clusters)))+geom_point())
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`ggplotly(ggplot(data=NULL,aes(x=tsne_sc3$Y[,1],y=tsne_sc3$Y[,2],col=rtsne_ph$Y[,1]))+geom_point())
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`ggplotly(ggplot(data=NULL,aes(x=tsne_sc3$Y[,1],y=tsne_sc3$Y[,2],col=exprs(sceset)["Dach1",]))+geom_point())
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`ggplotly(ggplot(data=NULL,aes(x=tsne_sc3$Y[,1],y=tsne_sc3$Y[,2],col=as.factor(pData(sceset)$sc3_8_clusters)))+geom_point())
We recommend that you use the dev version of ggplot2 with `ggplotly()`
Install it with: `devtools::install_github('hadley/ggplot2')`#plot_ly(x=tsne_sc3$Y[,1],y=tsne_sc3$Y[,2],z=rtsne_ph$Y[,1],color=as.factor(pData(sceset)$sc3_8_clusters))plot_ly(x=tsne_sc3$Y[,1],y=rtsne_ph$Y[,1],color=as.factor(pData(sceset)$sc3_8_clusters))No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-modeplot_ly(x=tsne_sc3$Y[,2],y=rtsne_ph$Y[,1],color=as.factor(pData(sceset)$sc3_8_clusters))
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-mode
No trace type specified:
Based on info supplied, a 'scatter' trace seems appropriate.
Read more about this trace type -> https://plot.ly/r/reference/#scatter
No scatter mode specifed:
Setting the mode to markers
Read more about this attribute -> https://plot.ly/r/reference/#scatter-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